Since its inception 30 years ago perhaps no innovation in radar technology has made such a tremendous impact on the field as that of radar interferometry. Radar interferometry uses two or more observations separated in either time or space to measure fraction of a wavelength scale range differences between the two observations. Radar interferometry is used by scientific, commercial and government institutions for numerous applications including topographic map generation, surface deformation mapping, landslide monitoring, current velocity measurement, vegetation structure determination and change detection. Radar interferometers can be flown on either spaceborne or airborne platforms or be fixed observing systems. This course is designed to provide an overview of the basic concepts of radar interferometry and an introduction to some of its applications. The course will cover basic radar imaging principles, a geometric and imaging signal perspective of the interferometric phase, interferometric correlation, basic sensitivity equations, phase unwrapping, topographic mapping and repeat pass interferometry for deformation measurements. The principles will be illustrated with examples from both spaceborne and airborne interferometric data sets. An overview of some of the major applications of radar interferometry will be presented with an emphasis on topographic and deformation mapping. The course will also briefly touch upon permanent scatter methods and polarimetric interferometry.

Radar interferometry

Freezing of gait (FoG) is a common gait deficit in advanced Parkinson's disease (PD). FoG events are associated with falls, interfere with daily life activities and impair quality of life. FoG is often resistant to pharmacologic treatment; therefore effective non-pharmacologic assistance is needed.

Online detection of freezing of gait with smartphones and machine learning techniques

Among Parkinson’s disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient’s treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy.

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Home detection of freezing of gait using Support Vector Machines through a single waist-worn triaxial accelerometer

Landslides susceptibility maps were constructed in the Pyeong-Chang area, Korea, using the Random Forest and Boosted Tree models. Landslide locations were randomly selected in a 50/50 ratio for training and validation of the models. Seventeen landslide-related factors were extracted and constructed in a spatial database. The relationships between the observed landslide locations and these factors were identified by using the two models. The models were used to generate a landslide susceptibility map and the importance of the factors was calculated. Finally, the landslide susceptibility maps were validated. Finally, landslide susceptibility maps were generated. For the Random Forest model, the validation accuracy in regression and classification algorithms showed 79.34 and 79.18%, respectively, and for the Boosted Tree model, these were 84.87 and 85.98%, respectively. The two models showed satisfactory accuracies, and the Boosted Tree model showed better results than the Random Forest model.

Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea

Automatic detection of freezing of gait events in patients with Parkinson's disease

Due to the aging process, the aftereffect of an accident or the problems in brain, some people can experience difficulties in walking. It could be helpful in the treatment of these individuals suffering walking difficulties to monitor their movement. The unconstrained activity monitoring system (U-AMS) was developed to monitor the body's acceleration. It can be attached easily and doesn't interfere with daily activity. The hardware consists of 3-axis accelerometer, which records data for long periods of time. The movement image was simultaneously recorded by a camcorder. To collect data from U-AMS and analyze them, several programs were developed. The data were transferred to a computer by RS-232 module and displayed through viewing program made by MFC (Microsoft, USA). The analyzing program, which links viewing program to MATLAB (Mathworks, USA), analyzes the data to detect movement disorder. In this paper, freezing, which is characterized as start hesitation., was the target disorder. Using the different characteristics in frequency domain between freezing and normal gait, freezing appearances were detected. By comparing with movement image, the accuracy of the freezing detection was verified to have a good performance.

Gait analysis for freezing detection in patients with movement disorder using three dimensional acceleration system

The measurement of precise along-track displacements has been made with the multiple-aperture interferometry (MAI). The empirical accuracies of the MAI measurements are about 6.3 and 3.57 cm for ERS and ALOS data, respectively. However, the estimated empirical accuracies cannot be generalized to any interferometric pair because they largely depend on the processing parameters and coherence of the used SAR data. A theoretical formula is given to calculate an expected MAI measurement accuracy according to the system and processing parameters and interferometric coherence. In this paper, we have investigated the expected MAI measurement accuracy on the basis of the theoretical formula for the existing X-, C- and L-band satellite SAR systems. The similarity between the expected and empirical MAI measurement accuracies has been tested as well. The expected accuracies of about 2–3 cm and 3–4 cm (γ = 0.8) are calculated for the X- and L-band SAR systems, respectively. For the C-band systems, the expected accuracy of Radarsat-2 ultra-fine is about 3–4 cm and that of Sentinel-1 IW is about 27 cm (γ = 0.8). The results indicate that the expected MAI measurement accuracy of a given interferometric pair can be easily calculated by using the theoretical formula.

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Theoretical Accuracy of Along-Track Displacement Measurements from Multiple-Aperture Interferometry (MAI)

The synthetic aperture radar (SAR) offset tracking method has been widely used for multitemporal analysis of fast glacier movements in the polar region. However, it can be severely distorted, particularly in the case of L-band SAR systems mainly due to a frequent occurrence of ionospheric effects in the polar region. In this study, we developed an efficient method to extract and correct the ionospheric contribution from SAR offset tracking measurements. The method exploits an iterative directional filtering approach, which is based on the pattern and directionality of ionospheric streaks. The measurement performance of the proposed method was evaluated by using three L-band advanced land observing satellite phased array type L-band synthetic aperture radar pairs. Our results showed that the proposed correction achieved the improved measurement accuracies from 4.68–23.88 to 1.03–1.51 m/yr. It means that the accuracies of corrected measurements were about 5–16 times better than those of the original measurements. From the results, we concluded that our correction technique is highly suitable for the precise measurement of the glacier displacements even in the presence of strong ionospheric effects. Using the proposed method, the variations of glacier velocities were measured in the Vylki, Shury, and Kropotnika glaciers on Novaya Zemlya, which is located in the Russian Arctic Ocean, and the grounding zones were detected from the measurements in the Shury and Kropotnika glaciers. It further confirmed that the proposed correction method is allowed for the precise monitoring of glacier movements. However, in cases of severe ionosphere-distorted measurements, the proposed method may be limitedly applied.

Ionospheric Correction of L-Band SAR Offset Measurements for the Precise Observation of Glacier Velocity Variations on Novaya Zemlya

Recently a multiple-aperture interferometry (MAI)-based azimuth shift method has been proposed to correct the ionospheric phase on synthetic aperture radar (SAR) interferograms. This method needs to determine integral constants required for azimuth integration. The exact estimation of the integral constants plays a key role in this MAI-based method. In this paper, we propose an efficient method for improving the performance of integral constant estimation, which functions by simultaneously removing the ionospheric and orbital phase artifacts from the interferometric SAR interferogram. We validate the performance improvement of the proposed method using two Advanced Land Observation Satellite Phase Array L-band SAR (ALOS PALSAR) interferometric pairs. The proposed method is compared with a MAI-based method, which does not work well due to azimuth integration errors. The proposed method successfully corrects the ionospheric and orbital phase artifacts. In addition, we compare the performances of the previous and proposed methods using the in situ Global Positioning System velocities. The root-mean-square error (RMSE) in line-of sight velocity from the previous method is about 7.0 mm/yr, whereas the RMSE from the proposed method is about 4.3 mm/yr. An RMSE reduction of about 38.6% is achieved when using the proposed method. These results indicate: 1) that the proposed method successfully estimates and corrects the ionosphere and orbital phase distortions; and 2) that the proposed method is superior to the previous method.

An Improvement of Ionospheric Phase Correction by Multiple-Aperture Interferometry

Park, S.-H.; Jung, H.-S.; Lee, M.-J.; Lee, W.-J., and Choi, M.-J., 2019. Oil spill detection from PlanetScope satellite image: Application to oil spill accident near Ras Al Zour area, Kuwait in August 2017. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 251-260. Coconut Creek (Florida), ISSN 0749-0208.Oil spill accidents are major marine disasters that can destroy the ocean ecosystem. Satellite images are widely used to respond immediately to oil spill accidents. A miniaturized satellite equipped with an optical sensor has an advantage that the imaging period is very short because a large number of satellites can be operated. In this study, oil slick areas were detected by applying the artificial neural network (ANN) technique to the PlanetScope satellite optical image captured near Ras Al Zour, Kuwait on August 10, 2017. However, the image included sunglint effects owing to ocean waves and yellow dust areas, making it difficult to classify the oil slick area. In addition, the spilled oil had three different spectral information characteristics through interaction with the sea surface of the Persian Gulf. The image processing was divided into the pre-processing step and the oil slick classification step to detect the oil slick area and to mitigate these limitations. In the pre-processing step, a directional median filter was applied to reduce the sunglint effects caused by ocean waves, and the negative effects from the sea surface and dust were mitigated using the differences with the low-pass filtered image. In the oil slick detection step, three types of oil probability maps and one sea surface probability map were produced using the ANN technique. Subsequently, an oil slick classification map was produced by applying the maximum probability choosing method and dust area rejection. Through validation, the overall accuracy of the oil classification map was obtained to be 82.01 % and the kappa coefficient was 72.42 %. The proposed oil detection method can effectively detect different types of oil spill areas in optical images with sunglint and dust handicaps. Moreover, it has high potential to be used in the future because the revisit time of the satellite image used is shorter than that of other optical images.

Won-Jin Lee

Papers

Gait analysis for freezing detection in patients with movement disorder using three dimensional acceleration system

Theoretical Accuracy of Along-Track Displacement Measurements from Multiple-Aperture Interferometry (MAI)

Ionospheric Correction of L-Band SAR Offset Measurements for the Precise Observation of Glacier Velocity Variations on Novaya Zemlya

An Improvement of Ionospheric Phase Correction by Multiple-Aperture Interferometry

Oil Spill Detection from PlanetScope Satellite Image: Application to Oil Spill Accident near Ras Al Zour Area, Kuwait in August 2017